Privacy-Aware and AI Techniques for Healthcare Based on K-Anonymity Model in Internet of Things

Sangaiah, Arun Kumar; Javadpour, Amir; Ja’fari, Forough; Pinto, Pedro; Chuang, Huan-Ming

doi:10.1109/tem.2023.3271591

Cited by 8 publications

(1 citation statement)

References 45 publications

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“…Some of the algorithms found during this research include the incognito mode algorithm, Samarati algorithm, and Sweeney algorithm. [10] For some comparison amongst this small selection of algorithms,…”

Section: Patient Data Privacymentioning

confidence: 99%

Artificial Intelligence Usage and Data Privacy Discoveries within mHealth

Schulte,

Engebretson,

Spanier

EPiC Series in Computing

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Advancements in artificial intelligence continue to impact nearly every aspect of human life by providing integration options that aim to supplement or improve current processes. One industry that continues to benefit from artificial intelligence integration is healthcare. For years now, elements of artificial intelligence have been used to assist in clinical decision making, helping to identify potential health risks at earlier stages, and supplementing precision medicine. An area of healthcare that specifically looks at wearable devices, sensors, phone applications, and other such devices is mobile health (mHealth). These devices are used to aid in health data collection and delivery. This paper aims at addressing the current uses and challenges of artificial intelligence within the mHealth field as well as an overview of current methods to help provide patient data privacy during data collection and storage.

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Section: Patient Data Privacymentioning

confidence: 99%